Objective This study aims to construct a multimodal fusion model (FM) based on CT and hematoxylin and eosin (H&E) stained slices to predict the PD-L1 expression in non-small cell lung cancer (NSCLC) and to explore its additional value in predicting the prognosis of immunotherapy. Materials and methods A retrospective analysis was conducted of 328 NSCLC patients with available PD-L1 immunohistochemical results. They were randomly divided into a training set, a validation set, and a test set in a 4:1:1 ratio. Radiomics and pathological models were constructed based on CT images and H&E slides, respectively, to predict PD-L1 expression, and then a radio-pathological FM was established. Then, the radio-pathological FM was used to generate predictive scores for an independent NSCLC immunotherapy survival validation cohort. Results A total of 55.5% (182/328) of patients were PD-L1 positive and included in the PD-L1 prediction cohort. Compared to the single-modality model, the radio-pathological FM achieved the highest predictive performance, with AUCs of 0.90, 0.80, and 0.73 across the three subsets, respectively. In the survival validation cohort, patients in the high-score group had significantly better progression-free survival (PFS) and overall survival than those in the low-score group. Furthermore, the FM score was an independent predictor of PFS. When combined with clinical factors, its C-index for predicting PFS was 0.74 (95% CI: 0.665–0.809). Conclusion For the first time, a radio-pathological FM was constructed to predict PD-L1 expression in NSCLC. The study also demonstrated the model’s potential for predicting patient prognosis under immunotherapy. Critical relevance statement This first fusion model combining CT radiomics and hematoxylin and eosin (H&E) deep learning non-invasively predicts programmed death-ligand 1 (PD-L1) and immunotherapy response in non-small cell lung cancer (NSCLC). Key Points <UnorderedList Mark="Bullet"> <ItemContent> The fusion model can accurately predict programmed death-ligand 1 (PD-L1) and immunotherapy outcomes in non-small cell lung cancer (NSCLC). </ItemContent> <ItemContent> The fusion model outperformed either single-modality model in distinguishing PD-L1-positive. </ItemContent> <ItemContent> Potential to reduce PD-L1 immunohistochemical testing and support precision immunotherapy decisions. </ItemContent> </UnorderedList> Graphical Abstract

A radio-pathological fusion model for predicting PD-L1 expression and immunotherapy response in non-small cell lung cancer

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2026年6月15日

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Dingpin Huang,
Fangyi Xu,
Yi Gan,
Liya Ding,
Kaihua Lou,
Yongcen Li,
Dong Xie,
Haiping Zhang,
Lei Shi,
Rui Xu,
Hongjie Hu

摘要

Objective

This study aims to construct a multimodal fusion model (FM) based on CT and hematoxylin and eosin (H&E) stained slices to predict the PD-L1 expression in non-small cell lung cancer (NSCLC) and to explore its additional value in predicting the prognosis of immunotherapy.

Materials and methods

A retrospective analysis was conducted of 328 NSCLC patients with available PD-L1 immunohistochemical results. They were randomly divided into a training set, a validation set, and a test set in a 4:1:1 ratio. Radiomics and pathological models were constructed based on CT images and H&E slides, respectively, to predict PD-L1 expression, and then a radio-pathological FM was established. Then, the radio-pathological FM was used to generate predictive scores for an independent NSCLC immunotherapy survival validation cohort.

Results

A total of 55.5% (182/328) of patients were PD-L1 positive and included in the PD-L1 prediction cohort. Compared to the single-modality model, the radio-pathological FM achieved the highest predictive performance, with AUCs of 0.90, 0.80, and 0.73 across the three subsets, respectively. In the survival validation cohort, patients in the high-score group had significantly better progression-free survival (PFS) and overall survival than those in the low-score group. Furthermore, the FM score was an independent predictor of PFS. When combined with clinical factors, its C-index for predicting PFS was 0.74 (95% CI: 0.665–0.809).

Conclusion

For the first time, a radio-pathological FM was constructed to predict PD-L1 expression in NSCLC. The study also demonstrated the model’s potential for predicting patient prognosis under immunotherapy.

Critical relevance statement

This first fusion model combining CT radiomics and hematoxylin and eosin (H&E) deep learning non-invasively predicts programmed death-ligand 1 (PD-L1) and immunotherapy response in non-small cell lung cancer (NSCLC).

Key Points

The fusion model can accurately predict programmed death-ligand 1 (PD-L1) and immunotherapy outcomes in non-small cell lung cancer (NSCLC).

The fusion model outperformed either single-modality model in distinguishing PD-L1-positive.

Potential to reduce PD-L1 immunohistochemical testing and support precision immunotherapy decisions.

Graphical Abstract